AIMC Topic: Vocalization, Animal

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Classification of Bryde's whale individuals using high-resolution time-frequency transforms and support vector machines.

The Journal of the Acoustical Society of America Mar 1, 2025
Whales generate vocalizations which may, deliberately or not, encode caller identity cues. In this study, we analyze calls produced by Bryde's whales and recorded by ocean-bottom arrays of hydrophones deployed close to the Costa Rica Rift in the Pana...
Animals Vocalization, Animal Fourier Analysis Whales Signal Processing, Computer-Assisted Acoustics Sound Spectrography Time Factors Support Vector Machine
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Rapid detection of fish calls within diverse coral reef soundscapes using a convolutional neural networka).

The Journal of the Acoustical Society of America Mar 1, 2025
The quantity of passive acoustic data collected in marine environments is rapidly expanding; however, the software developments required to meaningfully process large volumes of soundscape data have lagged behind. A significant bottleneck in the anal...
Vocalization, Animal Humans Signal Processing, Computer-Assisted Sound Spectrography Animals Coral Reefs Neural Networks, Computer Fishes Acoustics Time Factors United States Virgin Islands Sound
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The development of deep convolutional generative adversarial network to synthesize odontocetes' clicks.

The Journal of the Acoustical Society of America Jan 1, 2025
Odontocetes are capable of dynamically changing their echolocation clicks to efficiently detect targets, and learning their clicking strategy can facilitate the design of man-made detecting signals. In this study, we developed deep convolutional gene...
Acoustics Sound Spectrography Bottle-Nosed Dolphin Deep Learning Signal Processing, Computer-Assisted Generative Adversarial Networks Porpoises Vocalization, Animal Echolocation Neural Networks, Computer Animals
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Automated detection of Bornean white-bearded gibbon (Hylobates albibarbis) vocalizations using an open-source framework for deep learning.

The Journal of the Acoustical Society of America Sep 1, 2024
Passive acoustic monitoring is a promising tool for monitoring at-risk populations of vocal species, yet, extracting relevant information from large acoustic datasets can be time-consuming, creating a bottleneck at the point of analysis. To address t...
Vocalization, Animal Hylobates Borneo Acoustics Sound Spectrography Deep Learning Animals Signal Processing, Computer-Assisted
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Using deep learning to track time × frequency whistle contours of toothed whales without human-annotated training data.

The Journal of the Acoustical Society of America Jul 1, 2023
Many odontocetes produce whistles that feature characteristic contour shapes in spectrogram representations of their calls. Automatically extracting the time × frequency tracks of whistle contours has numerous subsequent applications, including speci...
Deep Learning Sound Spectrography Whales Vocalization, Animal Humans Algorithms Animals
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Biodiversity assessment using passive acoustic recordings from off-reef location-Unsupervised learning to classify fish vocalization.

The Journal of the Acoustical Society of America Mar 1, 2023
We present the quantitative characterization of Grande Island's off-reef acoustic environment within the Zuari estuary during the pre-monsoon period. Passive acoustic recordings reveal prominent fish choruses. Detailed characteristics of the call emp...
Ecosystem Unsupervised Machine Learning Acoustics Sound Biodiversity Animals Fishes Vocalization, Animal
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Silbido profundo: An open source package for the use of deep learning to detect odontocete whistles.

The Journal of the Acoustical Society of America Dec 1, 2022
This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck...
Vocalization, Animal Cetacea Deep Learning Sound Spectrography Software Animals
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Deep convolutional network for animal sound classification and source attribution using dual audio recordings.

The Journal of the Acoustical Society of America Feb 1, 2019
This paper introduces an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size ...
Neural Networks, Computer Callithrix Animals Sound Spectrography Signal Processing, Computer-Assisted Vocalization, Animal
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Convolutional neural network for detecting odontocete echolocation clicks.

The Journal of the Acoustical Society of America Jan 1, 2019
In this work, a convolutional neural network based method is proposed to automatically detect odontocetes echolocation clicks by analyzing acoustic data recordings from a passive acoustic monitoring system. The neural network was trained to distingui...
Echolocation Cetacea Animals Vocalization, Animal Neural Networks, Computer
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Automatic classification of grouper species by their sounds using deep neural networks.

The Journal of the Acoustical Society of America Sep 1, 2018
In this paper, the effectiveness of deep learning for automatic classification of grouper species by their vocalizations has been investigated. In the proposed approach, wavelet denoising is used to reduce ambient ocean noise, and a deep neural netwo...
Deep Learning Fishes Neural Networks, Computer Sound Vocalization, Animal Animals
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